1. Field of the Invention
The invention relates to software, systems and methods used to determine the coverage of cell sites for cellular phones and, more particularly, to a method and system used to generate and calibrate site specific coverage models.
2. Description of the Related Technology
Cellular radio systems provide wireless connections between portable cellular telephones and a cellular radio infrastructure of cell sites and interconnecting network facilities. In an ideal environment with uniform frequency usage and cell coverage, cell sites might be arranged in a honeycomb-like pattern to maximize individual cell utilization. However, such an ideal arrangement is seldom, if ever, applicable in real world environments. Instead, geographic coverage of cell sites is dictated by many factors, including density of users, topology, interference, and other factors. Thus, simulation systems are used to model cell sites and cellular networks as part of cellular network design, upgrade and maintenance procedures. However, because of the variability between and among even similar locations, models must be calibrated to conform to the actual planned cell site environment.
Hoque, U.S. Pat. No. 5,410,736, entitled xe2x80x9cMethod For Determining Radio Transmitter Sites With Signals That May Potentially Interfere With An Intended Signal At A Radio Receiver Sitexe2x80x9d, issued Apr. 25, 1995 describing a method for conducting radio transmission systems interference studies by modifying a conventional two-step process of conducting a simple analysis on all potentially interfering systems to eliminate those clearly not causing interference into radio receiver under study, and then conducting a detailed analysis on the remaining systems. The disclosure describes replacing the first step with a method using pre-calculated average terrain elevations over a geographic block for determining whether the loss should be calculated using a smooth terrain calculation method with a simulated single knife edge diffraction obstacle in the path, or a rough terrain calculation method that substitutes a pre-calculated block roughness factor in place of the path roughness factor. The disclosure also describes substituting a new effective antenna height for the actual antenna height in propagation loss calculations.
U.S. Pat. No. 5,787,350 to van der Vorm, et al. entitled xe2x80x9cMethod for Determining Base Station Locations, and Device for Applying the Methodxe2x80x9d issued Jul. 28, 1998 describing automated determination of base station locations by calculating, for each location, a number which is a function of a parameter associated with that location (telephone traffic, field strength, motor traffic) and of parameters belonging to adjoining locations, and by assigning a base station to the location having the most extreme number. Then the parameter associated with that location, and the parameters associated with adjoining locations are each adjusted on the basis of an adjustment function associated with the base station, and new numbers are calculated which are a function of the new, adjusted parameters, etc.
Brockel, et al., U.S. Pat. No. 5,794,128, entitled xe2x80x9cApparatus and Processes For Realistic Simulation Of Wireless Information Transport Systemsxe2x80x9d issued Aug. 11, 1998 describing models and processes for simulating wireless information transport systems using time and frequency dynamic effects on stationary and mobile communications systems. A modeling system includes a data entry module, a communications traffic selection module, a driver database, and voice and data input modules furnishing a simulation input to a network simulation module. The network simulation module has communications xe2x80x9crealismxe2x80x9d effects, a it distributed interactive simulation structure, a channel error-burst model to transmit random errors, and a multipath modeling module to integrate deterministic and stochastic effects. The multipath modeling module, having a digital radio model and a Terrain-Integrated Rough Earth Model, influences the simulation inputs forming a multipath output, which is adjusted by voice and data inputs to provide a real-time simulation output signal to a module displaying the simulated communications network and link connectivity.
Lee, et al., U.S. Pat. No. 6,032,105 entitled xe2x80x9cComputer-Implemented Microcell Prediction Modeling with Terrain Enhancementxe2x80x9d issued Feb. 29, 2000 describing a computer-implemented modeling tool for cellular telephone systems that predicts signal strength by considering the effects of terrain and man-made structures on transmitted signals. The modeling tool gives predictions under line of sight conditions, when obstructions occur due to terrain contours, and when mobile or transmitter antennas are blocked by buildings or other structures.
As described in these four disclosures, all of which are incorporated herein by reference in their entirety, various methods and techniques are used to model cellular telephone system operation including predicting coverage of each of the radio transceiver cell sites forming the mosaic network of microwave frequency radio stations communicating with the portable cellular telephones. Such models are critical because, although the cellular service provider measures the signal strength directly, individual measurements would not enable the provider to know the signal strength at every point within the cell to confirm cell coverage and identify and address problem locations.
Unlike theoretical free space propagation, actual signal depends on local up environmental characteristics within the cell. Cellular service providers use models to estimate the signal strength at any point within the cell. These models predict system coverage and potential interference at points within the cell by determining the signal path loss from the cell site to the specific point within the cell. Cellular service providers use this information for a variety of purposes including initial cell site location, placement of addition cell sites, frequency planning, and to determine the power required at specific sites.
Many factors are included in the determination of signal path loss to a specific point within the cell. Three main concerns are transmission, environment and losses due to multiple signal paths (multi-path) causing self-destructive interference. Transmission modeling is used to predict the power available from the antenna at locations within the intended cell site coverage space. In general, the amount of power at the output of the antenna is a function of the amount of power provided to the antenna and the antenna radio frequency radiation pattern. These two factors, power output and antenna gain, sometimes expressed as Effective Radiated Power (ERP), are crucial in determining the signal strength along various radials from the antenna.
Methods for calculating ideal transmission loss are well known. Transmitter power output, transmission cable loss, antenna gain, free space propagation loss, antenna and receiver gain can all be calculated and used to predict a theoretical, best case cell coverage.
Environment modeling involves determining the effects of the terrain features between the cell site and the specific position within the cell. (Contrary to its designation, environmental modeling at typical cellular radio operating frequencies does not normally encompass weather conditions such as humidity, precipitation, temperature, etc.) While signal path losses attributable to dispersion increase as the inverse square of the distance from the cell site increases, environment factors can greatly affect these losses. Modeling of the environment includes the signal reduction due to the distance from the cell site as well as defraction losses caused by buildings or other terrain features between the cell site and the specific point within the cell. Furthermore, since radio propagation conditions vary significantly in typical operating environments, signal path loss models normally account for the statistical variability of the received signal (which is defined as environmental shadowing) by incorporating suitable power margins (offsets) for the purpose of system planning.
A third type of modeling predicts the effects of multiple signal paths and resultant destructive interference at the received location, namely multi-path fading. Multi-path fading results from multiple paths taken by a signal from the cell site to a specific point within a cell. When two or more signal components arrive at a particular reception point in space after traveling different distances, the resultant signals may no longer be in phase. Thus, when these signals are combined, the difference in the phase shifts may combine destructively and produce a degraded sum signal at the specific point. Unfortunately, precise modeling of destructive interference is very difficult because of the number of variables involved and the relatively short 15.1 to 31.2 centimeter wavelengths used by the cellular services. Accordingly, for system planning purposes, power margins (offsets) are normally included in path loss predictions to account for the effects of multi-path fading.
To determine the signal path loss from the cell site to a specific point within the cell, signal path loss equations used by cellular service providers account for transmission and environment losses, and include power margins to account for multi-path fading and environmental shadowing. Cellular service providers may use cell coverage equations from generally accepted signal path loss equations or generate their own proprietary formulae. In either case, once selected, the equations must be calibrated to accurately model a specific cell site. Typical calibrations include calculation of values for geographical environment parameters to account for factors such as, the morphology (e.g. urban, suburban and rural), height differences between the transmitter and remote receiver, and the density and height of terrain features between the two.
As described, the effective planning of cellular networks necessitates the use of suitable models for predicting coverage and interference. Numerous models have been developed and described in the literature. See, for example, IEEE Vehicular Technology Society Committee on Radio Propagation: xe2x80x9cSpecial Issue on Mobile Radio Propagationxe2x80x9d, IEEE Transactions on Vehicular Technology, vol. VT-37, no. 1, February 1988, pp.3-72. These models are typically semi-deterministically or empirically based and therefore must be calibrated for specific environments (i.e. a model calibrated for urban Tokyo is likely to be different from that of rural Texas). The calibration process involves modifying the model parameters to accurately approximate relevant measurement data. Typically the propagation models include parameters that account for the geographical environment, e.g. whether the environment is urban or rural, the ground height relative to the transmitter and the terrain between the transmitter and receiver. This environmental information can be obtained from a Geographical Information System (GIS) and should be included in the analysis.
Cellular service providers may use propagation measurement data to calibrate these signal path loss equations. Propagation measurement data is obtained through actual field measurements taken at various locations throughout the cell. Precise measurement locations may be determined using a Global Positioning System (GPS). Typically, a large number of field measurements may be required to accurately calibrate a modeling equation. Once the raw data is collected, it is converted to the appropriate format and used to individualize the cell site to its location.
The calibration process uses the field data collected to define parameters, variable coefficients and constants of equations used to model cell coverage. The calibration is a laborious manual, procedure requiring the significant time and effort of someone skilled in the art.
Automated calibration processes may use basic linear regression techniques on each of the model parameters. See, for example, Bernardin P., et. al.: xe2x80x98Cell Radius Inaccuracy: A New Measure Of Coverage Reliabilityxe2x80x99, IEEE Trans. Veh. Techn., vol. 47, no. 4, November 1998, pp.1215-1226. However these techniques exhibit two significant problems.
A first problem is caused by variability in the measurement data that can bias the calibration process to produce a model with results falling outside of the set of physically realizable solutions, i.e., a model that effectively defies the laws of physics. For example, the signal attenuation in a cellular environment can be attributed to signal dispersion and sometimes to losses due to signal diffraction and reflection. Accordingly, the minimum loss (in the far field) is equivalent to that associated with signal dispersion, which is defined as the free space loss. Since the free space loss represents a lower bound that cannot be explicitly included in a basic linear regression process, sometimes models that do not make physical sense are generated.
A second problem results from a fundamental assumption of linear regression that the model parameters are uncorrelated, and can therefore be solved independently. That is, each parameter should be independent of variations in the other parameters. However in practice the propagation models used for cellular environments contain parameters that are correlated, for example the diffraction loss is generally correlated with the effective height of the receiver. Accordingly, linear regression can only be used reliably for model calibration when there is a low correlation between the model parameters.
Because of the shortfalls with the existing calibration processes, cellular propagation models are commonly calibrated manually. This technique includes the xe2x80x9cartful weakingxe2x80x9d of parameter values in repeated attempts to conform the model to the actual field measurements. As expected, interative manual calibration is difficult, time consuming and error prone. In addition, the process may produce hidden anomalies such as singularities in the solution set that might go unnoticed during a manual calibration process but which might produce erroneous predictions when the model is implemented.
Accordingly, a need exists for an automatic calibration device and method of calibrating a radio frequency coverage model to reflect environmental factors and accurately reflect field test data. A need further exists for a method of calibrating a cellular system model that accommodates correlated parameter variables. A further need exists for a cellular model that avoids erroneous solutions attributable to perceived or actual local minimum in favor of global minimum.
In view of the above, a need exists for an automated equation calibration system and method that provides solutions consistent with physically realizable solutions sets defined by accepted laws and principles of radio transmission theory and other laws of physics, and accommodates correlated variables. A further need exists for a calibration model that includes additional system characteristics to minimize or avoid any manual determination of the calibrated equations.
These and other objects, features and technical advantages are achieved by a system and method that represents measurement and associated data in a matrix form. An automated method is used to calibrate the equations and then evaluate and accept or reject the calibrated equations. The calibrated equations are adjusted if necessary. The user can review the decision to accept the calibrated equation or may adjust the measurement data used in the calibration of the modeling equation. The final calibrated equations are then stored for later use. Models may include the storage of measurement and related information associated within a single row of a matrix. The user may delete specific measurements and associated data from consideration, select or generate specific modeling equations, review calibrated equations or judge criteria, adjust differences between successive calibration equations. A second order gradient search may be designated as the default calibration scheme with the use of a pseudo-exhaustive search for a secondary calibration scheme.
The foregoing has outlined rather broadly the features and technical advantages of the present invention in order that the detailed description of the invention that follows may be better understood. Additional features and advantages of the invention will be described hereinafter which form the subject of the claims of the invention. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present invention. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as set forth in the appended claims.